Multi - Category & Multi - Criteria Recommendation System using Collaborative Based Filtering
Abstract: Recommendations Systems have become one of the most popular application of data science today. It predicts or offers products to customers based on their past browsing history or purchases. Although a lot of effort, research and time has been spent on recommendation engines, we are yet to truly unlock their potential. At the core, a recommender system employs a machine learning algorithm whose job is to predict user's ratings for a particular entity. Through this project, we are employing a multi category recommendation system which will give the user recommendations across different categories based on the user data of multiple categories consisting of different attributes. The concept of K - Nearest Neighbor Algorithm is implemented to derive the similarity of unknown entities or users based on past ratings of a particular entity. The implementation is carried out using JavaScript in Node, thereby extending the capabilities of Collaborative based filtering Algorithm to multiple categories.
Keywords: Multi Criteria Recommender System (MCRS), K - Nearest Neighbor Algorithm (KNN), Similarity Score, Collaborative - based filtering algorithm
How to Cite?: Shubham Gaur, Rishabh Naulakha, Khushal Hanswal, Meenakshi Sharma, Abhishek Mohanty, "Multi - Category & Multi - Criteria Recommendation System using Collaborative Based Filtering", Volume 10 Issue 8, August 2021, International Journal of Science and Research (IJSR), Pages: 1076-1080, https://www.ijsr.net/getabstract.php?paperid=MR21824001401, DOI: https://dx.doi.org/10.21275/MR21824001401